Hand Movement Recognition and Salient Tremor Feature Extraction With Wearable Devices in Parkinson's Patients.

Tremor is one of the earliest signs of Parkinson’s disease (PD), which seriously disrupts patients’ daily lives. It is important to study upper limb tremors quantitatively to control PD progression. In this study, surface electromyography (sEMG) signals from wearable devices are used to recognize rest, posture, and kinetic tremor action from 6 upper-limb clinical actions and to quantify features of tremors. A multivariable time series classification model (MTSCM) based on fully convolutional networks and a long short-term memory network is proposed to recognize tremor actions. MTSCM achieves a high degree of accuracy both on the left-hand and right-hand data sets for tremor actions. An improved Hilbert-Huang transform (HHT) method is proposed to decompose the inertial signals of tremor actions to obtain tremor components. Using the improved HHT, tremor and motion components can be decomposed effectively. In addition, 53 features are extracted from inertial and sEMG signals, and a canonical correlation analysis is used to determine the correlation between features and MDS-UPDRS scores. Several of the relevant characteristics are related to MDS-UPDRS scores, notably the dominant frequency and amplitude of the tremor component are significantly correlated (p < 0.01) with tremor scores. Detecting upper-limb clinical tremors in PD patients using wearable sensors is feasible according to our findings.